Scheduling fixed-priority tasks with preemption threshold
Why this work is in the frame
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Bibliographic record
Abstract
In the context of fixed-priority scheduling, feasibility of a task set with non-preemptive scheduling does not imply the feasibility with preemptive scheduling and vice versa. We use the notion of preemption threshold, first introduced by Express Logic, in their ThreadX real-time operating system, to develop a scheduling model that subsumes both preemptive and non-preemptive fixed priority scheduling. Preemption threshold allows a task to only disable preemption of tasks up to a specified threshold priority. Tasks having priorities higher than the threshold are still allowed to preempt. With this new scheduling model, we show that schedulability is improved as compared to both the preemptive and nonpreemptive scheduling models. We develop the equations for computing the worst-case response times, using the concept of level-i busy period. Some useful results about the generalized model are presented and an algorithm for optimal assignment of priority and preemption threshold is designed based on these results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it